Impact and cost-effectiveness of short-course tuberculosis preventive treatment for household contacts and people with HIV in 29 high-incidence countries: a modelling analysis

Summary Background Guidelines and implementation of tuberculosis preventive treatment (TPT) vary by age and HIV status. Specifically, TPT is strongly recommended for people living with HIV/AIDS (PLWHA) and household contacts younger than 5 years but only conditionally recommended for older contacts. Cost remains a major barrier to implementation. The aim of this study was to evaluate the cost-effectiveness of TPT for household contacts and PLWHA. Methods We developed a state-transition model to simulate short-course TPT for household contacts and PLWHA in 29 high-incidence countries based on data from previous studies and public databases. Our primary outcome was the incremental cost-effectiveness ratio, expressed as incremental discounted costs (2020 US$, including contact investigation costs) per incremental discounted disability-adjusted life year (DALY) averted, compared with a scenario without any TPT or contact investigation. We propagated uncertainty in all model parameters using probabilistic sensitivity analysis and also evaluated the sensitivity of results to the screening algorithm used to rule out active disease, the choice of TPT regimen, the modelling time horizon, assumptions about TPT coverage, antiretroviral therapy discontinuation, and secondary transmission. Findings Between 2023 and 2035, scaling up TPT prevented 0·9 (95% uncertainty interval 0·4–1·6) people from developing tuberculosis and 0·13 (0·05–0·27) tuberculosis deaths per 100 PLWHA, at an incremental cost of $15 (9–21) per PLWHA. For household contacts, TPT (with contact investigation) averted 1·1 (0·5–2·0) cases and 0·7 (0·4–1·0) deaths per 100 contacts, at a cost of $21 (17–25) per contact. Cost-effectiveness was most favourable for household contacts younger than 5 years ($22 per DALY averted) and contacts aged 5–14 years ($104 per DALY averted) but also fell within conservative cost-effectiveness thresholds in many countries for PLWHA ($722 per DALY averted) and adult contacts ($309 per DALY averted). Costs per DALY averted tended to be lower when compared with a scenario with contact investigation but no TPT. The cost-effectiveness of TPT was not substantially altered in sensitivity analyses, except that TPT was more favourable in analysis that considered a longer time horizon or included secondary transmission benefits. Interpretation In many high-incidence countries, short-course TPT is likely to be cost-effective for PLWHA and household contacts of all ages, regardless of whether contact investigation is already in place. Failing to implement tuberculosis contact investigation and TPT will incur a large burden of avertable illness and mortality in the next decade. Funding Unitaid.


Modeled Countries
Appendix Table 1 shows additional details on the TB and HIV epidemics in the 29 modeled countries. Data from WHO 1 and UNAIDS 2 *Cambodia and Zimbabwe are instead categorized as "global TB watchlist" countries.

Appendix
** Recent prevalence estimates are not available from UNAIDS' database.

Household Contacts
We modeled annual cohorts of household contacts consisting of each of three age groups (< 5, 5-14, and ≥ 15 years old); the size of each cohort was based on the estimated number of TB notifications (all forms) and the average household size (by age group) in each country. We included contacts of people diagnosed with both pulmonary and extrapulmonary TB. To estimate annual TB notifications, we assumed that TB incidence would decline by 2% annually (from estimated country-specific 2021 incidence levels) and that the percent of people with TB that are notified would remain constant (again compared to estimated 2021 country-specific treatment coverage ratios). 3 Data on household sizes and the household-age distribution came from a United Nations Population Division database made up of country census and household survey data. 4 For lack of better data, we assumed that the composition of households with TB would be similar to the average household in each country.
The proportion of each household contact cohort that has active TB disease (active), latent TB infection (LTBI), or no TB infection/disease (no TB) was based on estimates of active and latent TB prevalence from household contact studies (see main text Table 1 and Parameter Details section of the appendix for more details on all parameter estimates). 5 In the TPT scenario, a percentage of each cohort receives a contact investigation visit. For household contacts, the percentage receiving visits increases linearly from 0% in the year before the first year of the model (2022), to 9% in the first year (2023), up to 90% in the tenth through thirteenth years . TPT initiation was then based on the proportion of household contacts that screen/test negative for active disease (and are thus eligible for TPT) and estimates of the TPT initiation ratio among investigated household contacts that do not have active TB disease. 6 Contacts < 5 are assumed to be screened for symptoms only (main text Figure 1A). Those that screen positive are tested for active disease using chest Xray (CXR), and those with abnormal CXR are presumed to have TB disease and initiate TB treatment. Everyone else (negative symptom screen and/or normal CXR) is eligible to initiate TPT. Contacts ≥ 5 are assumed to be screened for symptoms and screened with CXR (main text Figure 1B). Those with abnormal CXR and/or positive symptom screen are tested for active disease using Xpert, and those with a positive Xpert are presumed to have TB disease and initiate TB treatment. Everyone else (negative symptom screen and normal CXR, or negative Xpert) is eligible to initiate TPT.
The proportions of household contacts that screen positive/negative and test positive/negative are based on symptom screening, CXR, and Xpert sensitivity and specificity 7-9 (which vary by age), prevalence survey data 10 (on the proportions of populations that screen positive on either CXR or symptom screen), contact investigation data [11][12][13] (on the proportions of child contacts that screen positive on symptom screen) and the proportions of each cohort that have active TB, LTBI, or no TB from household contact studies 5 (discussed above). Costs are incurred per person screened (contact investigation cost and, for contacts ≥ 5, CXR costs) and per person tested (CXR costs for contacts < 5, Xpert costs for contacts ≥ 5).
Contacts that screen and test positive (including true and false positives) initiate treatment (we assume no dropout among symptomatic contacts). Both true positive and false positives incur the costs of treatment; true positives who do not fail treatment (based on country-specific treatment success ratios, see Parameter Details section) are assumed to be cured of TB disease (transition to "no TB" state), false positives with LTBI are assumed to clear their TB infections (transition to "no TB" state), and false positives without LTBI receive no benefit from treatment. Country-specific treatment costs were based on country-specific WHO data on drug-susceptible TB treatment unit costs, but were varied widely in a focused one-way sensitivity analysis. 14 A proportion of contacts that screen negative or test negative (including true and false negatives) initiate TPT. 6 False negatives (i.e., active TB that remains undetected) continue to have TB disease regardless of whether they are initiated on TPT, as do true positives that fail treatment, and are only treated based on age-specific background notification rates in each country (see Parameter Details section). Everyone who initiates TPT incurs the full cost of a TPT regimen, regardless of completion or benefits. A proportion of true negatives with LTBI that initiate TPT clear their infections; that proportion is based on TPT efficacy 15,16 and their TPT completion. True negatives without LTBI receive no benefits from TPT.
The proportion of TPT initiators who complete TPT was based on estimates of TPT completion [17][18][19][20][21] and the frequency of adverse events (e.g., hepatotoxicity, hypersensitivity) that result in TPT discontinuation [21][22][23] (main text Figure 1D). We assumed that all modeled toxicity events result in TPT discontinuation (with no benefits from TPT) and incur the costs of laboratory tests 24 and an outpatient visit 25 ; severe toxicity events additionally incur the cost of three inpatient bed days. 25 We assumed that those with no toxicity events that initiate but do not complete TPT have half the probability of clearing their infections as those that complete TPT (i.e., 50% relative efficacy).
In the No TPT scenario, no contact investigations or TPT initiations were assumed to occur, and active TB could only be cured based on country-and age-specific background notification ratios multiplied by country-and agespecific treatment success ratios (see Parameter Details section). Each cycle, active TB that is not detected (or is detected but not cured/does not complete treatment) remains active.
After modeling contact investigation, TPT initiation and completion (and resulting cures), and active TB treatment from contact investigation (and resulting cures), cohorts enter a Markov state-transition model that tracks their TB status annually (main text Figure 1E). Each annual timestep, percentages of each cohort can be notified and treated for active disease (active transition to no TB; incurring TB treatment costs), progress from LTBI to active disease that is subsequently notified and cured (LTBI transition to no TB; incurring TB treatment costs) or remains unnotified (LTBI transition to active), die of TB (if active) or non-TB (any state) causes, or remain in their current state. Progression from LTBI to active disease depends on age and time since entering the model (i.e., for most contacts, time since infection) (see Parameter Details section). 15,26,27 Treatment of active TB depends on background age-specific notification and successful treatment ratios (see Parameter Details section). TB mortality depends on age and treatment status. 28

People living with HIV/AIDS
We modeled both people living with HIV/AIDS (PLWHA) that are newly enrolled on ART (new) and PLWHA that have already been established on ART but have not yet initiated TPT (established). The size of the new and established PLWHA populations are estimated based on current numbers of adult PLWHA on ART, estimated HIV incidence, and reported data on the numbers of PLWHA that have already initiated TPT. Country-specific data on the size of the adult PLWHA population enrolled on ART in 2021 were obtained from UNAIDS. 29 We added to this estimates of HIV incidence from 2019-21, assuming a 2 year delay between incidence and ART enrollment and assuming that, going forward, countries meet the targets of 90% of PLWHA knowing their HIV status and 90% of those that know their status enrolling in care (81% overall ART coverage). For 2022 and beyond, we estimated HIV incidence by calculating the average increase or decline in HIV incidence from the past five years, based on UNAIDS estimates, and assuming this trend continues for the next ten years. These calculations yielded estimates of the size of the previously-enrolled PLWHA population in 2023 and the size of the new PLWHA population in 2023-35. To estimate the size of the established (but not yet initiated on TPT) population in 2023, we subtracted out the total number of PLWHA enrolled on TPT in each country from 2015 onward, based on data reported to the WHO 3 , assuming an annual mortality probability of 3·7% 30 (to account for PLWHA that enrolled in TPT but subsequently died and thus should not be subtracted out of the total number of PLWHA still alive in 2023). This adjustment yielded estimates of the size of established PLWHA population in 2023.
In 2023, we initiated the model with these two populations (new and established). In subsequent years (2024- 35), the size of the new PLWHA population is also based on the above calculations. The size of the established population that could be eligible to initiate TPT in subsequent years was estimated directly from modeled output for the previous year (details in subsequent paragraphs).
Each annual timestep, a percentage of both the new and established PLWHA populations are offered TPT. Similar to household contacts, in the TPT scenario, this percentage increases linearly from 0% in the year before the first year of the model (2022), to 9% in the first year (2023), up to 90% in the final four years (2032-35). In the No TPT scenario, TPT coverage is fixed at 0% for all years. We assumed that TB disease among PLWHA will be detected and treated (and subsequently cured) through screening at routine HIV-related health facility visits, and that TPT can be initiated during one of these visits for any PLWHA that do not have active disease ( Figure 1C). Because active disease screening and TPT initiation are assumed to be done through routine care and thus incur no additional costs, we did not explicitly model a TPT initiation/acceptance probability (that would allow us to back-calculate the number of PLWHA offered TPT based on each coverage level as we did for household contacts) (i.e., we acknowledge that there is some < 100% chance that those offered TPT initiate it, but because this does not affect costs or outcomes, we did not include it in the model).
LTBI prevalence among PLWHA was based on published country-specific LTBI estimates (see Appendix Table  2). 31 We assumed PLWHA without LTBI receive no benefit from TPT. The impact of TPT for PLWHA with LTBI depends on efficacy 16 and completion. As with household contacts, the proportion of TPT initiators who complete TPT was based on estimates of TPT completion [17][18][19][20][21] and the frequency of adverse events (e.g., hepatotoxicity, hypersensitivity) that result in TPT discontinuation [21][22][23] (main text Figure 1D). We assumed that all modeled toxicity events result in TPT discontinuation (with no benefits from TPT) and incur the costs of laboratory tests 24 and an outpatient visit 25 ; severe toxicity events additionally incur the cost of 3 inpatient bed days. 25 We assumed that those with no toxicity events that initiate but do not complete TPT have half the probability of clearing their infections as those that complete TPT (i.e., 50% relative efficacy).
After modeling screening for active disease and TPT initiation and completion each year, disease progression, mortality, and ART status are tracked via a state-transition Markov model (main text Figure 1E). The probability of progression from LTBI to active disease depends on time since entering the model, ART status, and time on ART upon entering the model (see Parameter Details section). 16,17,26,27,32 Detection and treatment among those that progress depends on ART status; all those on ART are assumed to be detected through routine care, while only some of those not on ART are detected, based on background adult notification rates (see Parameter Details section). Those who are notified are cured based on country-specific probabilities of treatment success for PLHIV (based on WHO treatment outcomes data). We further assumed that background notification of PLWHA not on ART with active disease would result in them being reconnected to care (transition back onto ART). TB mortality (among those that progress) and non-TB mortality (among everyone) also depends on ART status, and non-TB mortality is also assumed to be higher for those newly initiated on ART compared to those already established. [33][34][35][36][37][38] Transitions off ART are based on cohort studies of PLWHA [35][36][37][38] ; we modeled higher ART discontinuation and lossto-follow-up in the first year on ART than in subsequent years. Transitions back on to ART were calibrated so that the overall modeled proportion of PLWHA on ART would equal 90% (consistent with the UNAIDS target for 90% of PLWHA that know their HIV status to be on ART). Because of uncertainty in ART discontinuation and return probabilities, we also conducted a sensitivity analysis with less frequent ART-related transitions (all discontinuation occurs in the first year on ART, with a 10% discontinuation risk and no subsequent returns to care). Those on ART incur annual ART costs consisting of drugs, 2 viral load tests, 4 outpatient visits, and 10% overheads. 39 The modeled number of PLWHA on ART that have not initiated TPT at the end of a given year then forms the eligible established population for the following year (i.e., we assume that PLWHA not on ART would not initiate TPT until they returned to care).

Parameter Details
All parameter estimates are listed in the main text (Table 1), and the Model Details section (above) describes how these parameters were used in the model. Country-specific parameter estimates are shown in Appendix Table 2. The distributions we sampled from for each parameter in the probabilistic sensitivity analysis are shown in Appendix  Table 3. Descriptions of a few estimated parameters that were not taken directly from the literature and are not described in the previous section are included below.

Background probabilities of detection and treatment
Background notification probabilities (also known as treatment coverage ratios) by age and country were calculated by dividing reported age-specific notifications by estimated age-specific incidence, after subtracting out a share of estimated notifications that are from contact investigation from the numerator. 3 We also subtracted estimated misdiagnoses (false positives) from the numerator for children < 15 years, assuming that 19·4% of notifications among children < 5 years are false positives and half of this (9·7%) of notifications among children aged 5-14 years are false positives. 40 We incorporated uncertainty in misdiagnoses by sampling an < 5 misdiagnosis probability parameter from a beta distribution fit to 19·4% [14·4-24·8%], accounting for uncertainty reported in the source study and widening this uncertainty to account for generalizability across settings. We incorporated uncertainty in age-specific incidence by fitting gamma distributions to incidence means and confidence intervals estimated by the WHO. 3 We incorporated uncertainty in the proportion of contact investigations that would have been diagnosed routinely anyway (if not found by contact investigation) via an uninformed beta distribution (mean 50%, 95% CI [21-79%]). We sampled from these respective distributions 50,000 times to calculate 50,000 background notification probability samples for each age group and country, thus propagating uncertainty in misdiagnoses, true TB incidence, and notifications absent any contact investigation.
We multiplied the background notification probability samples by successful treatment probability samples to estimate the background probability of being detected and successfully treated. The probability of treatment success (completion and/or documented cure) was based on data on treatment outcomes reported to the WHO for each country. We sampled from beta distributions for each country, with the alpha parameter (numerator) equal to the number of documented treatment successes among child or adult cohorts in 2019 (newrel_014_succ and newrel_succ -newrel_014_succ, respectively, in the WHO outcomes database) and denominator equal to the outcomes cohort size (newrel_014_coh and (newrel_coh -newrel_014_coh), respectively) (beta parameter equals denominator minus numerator). For countries for which treatment outcomes were not reported, we drew 50,000 samples from all the other countries' samples combined. For PLHIV, treatment success probabilities were estimated similarly (based on tbhiv_coh and tbhiv_succ in the WHO database), while background notification probabilities were assumed to be 100% for PLHIV on ART and equal to adult background notification probabilities for PLHIV not on ART.
The resulting adjusted age-specific and country-specific probabilities of detection and successful treatment are shown in Appendix Table 2.

Country-Specific Costs
Country-specific costs estimates, along with other country-specific parameters, are shown in Appendix Table 2. Additional details on how these costs were estimated are provided below.

Contact investigation and CXR costs
To estimate country-specific contact investigation and CXR costs for all 29 countries, we searched the literature for published cost data that would cover each of the WHO geographic regions represented by the countries (Africa, Western Pacific, Southeast Asia, Americas, Europe). 3 For Pakistan and Somalia (which are in the Eastern Mediterranean region), we used Southeast Asia cost data and Africa cost data, respectively. We identified regionspecific estimates of the cost of conducting CXR [41][42][43][44][45] and the cost of household contact investigation 41,46-49 and converted the cost estimates to 2020 USD using consumer price index data from the International Monetary Fund. 50 We then estimated country-specific prices by scaling the regional prices by relative Gross National Income per capita (GNI p.c.). 51 For contact investigation we scaled costs 1:1 with GNI p.c. and for CXR we scaled 1:4 with GNI p.c. since we assumed that the majority of contact investigation costs would be determined by local wages, while evidence indicated that consumables and equipment (such as CXR machines themselves, which are typically internationally-traded commodities) made up much of CXR costs and, furthermore, we did not observe much correlation between countries' income levels and reported CXR costs. [43][44][45] Costs of Xpert and laboratory tests for adverse events We assumed Xpert and lab test costs would be similar across countries and thus did not vary them across countries in our analysis. Estimates of the cost of Xpert came from a recent multi-country economic evaluation study of Xpert in southern Africa 44 , while estimates of the cost of laboratory tests for suspected hepatotoxicity came from the South Africa National Health Laboratory Service price lists (we included the costs for ALT, full blood count, urine dipstick, urine sodium, and added 50% [0-100%] for labor and overheads). 24 We conservatively applied the cost of laboratory tests for suspected hepatotoxicity to all adverse events.

Inpatient and outpatient visit costs
Country-specific estimates of the cost per outpatient visit and the cost per inpatient bed day came from WHO CHOICE estimates. 25 We converted the estimates, which were reported in 2010 international dollars, to 2010 local currency, inflated them to 2020 local currency, and then converted them to 2020 USD. 50,51 When estimates were not reported for any particular country, we used regional estimates instead.

TB treatment costs
We used WHO estimates of TB treatment unit costs, which represent the median cost per person treated with drugsusceptible TB in a country. 14 We used drug-susceptible TB costs because, in the countries with high HIV prevalence, most new TB incidence is drug-susceptible, and because the analysis is assumed to cover household contacts of people with drug-susceptible TB only.

Drug costs
Based on estimates from our coauthors at Aurum Institute, we assumed that a full course of 3HP would cost $6 per person < 5 years old, $12 per person aged 5-14 years, and $13·5 per person aged 15 and above. We also costed an outpatient monitoring visit (and for household contacts, an initiation visit). In scenario analyses of 1HP, we assumed a full course of 1HP would cost $22 for all ages and would require an outpatient visit. To account for drug wastage, we assumed that full TPT courses would be set aside for all TPT initiators (to avoid stockouts), regardless of TPT completion.
We estimated an annual ART cost of $63 (dolutegravir, lamivudine, and tenofovir) based on recent price reports. 39 We also included the costs of two viral load tests (at $12 each) and 4 outpatient visits annually. While ART, viral load tests, and monitoring visits are likely to be borne by HIV programs, not TB programs, and are mostly unaffected by the introduction of TPT, some increases in spending are expected from keeping PLWHA on ART who would have otherwise died of TB absent TPT We added a 10% [6-14%] markup for overheads to all drug costs, based on two published studies that captured evidence on drug cost overheads (such as transportation, insurance, and quality control). 52,53 Appendix Values in the table indicate means and 95% uncertainty intervals. Details regarding parameter sources and estimation are provided in the main text (Table 1) and in the pages 3-8 of this supplement. Costs are all presented in 2020 USD. "CXR" = chest Xray, "LTBI" = latent tuberculosis infection. We sampled from the distributions shown in Table 1 by fitting gamma distributions to the reported means and standard deviations on chest Xray, inpatient bed day, and outpatient visit costs, normal distributions to the reported means and standard deviations on contact investigation and TB treatment unit costs, and beta distributions to the reported means and 95% confidence intervals on latent TB prevalence. More details regarding the estimation of background notification and treatment success probabilities are shown on pages 6-7. Ages of household contacts are displayed in years. For beta-distributed quantities, the alpha and beta parameters are shown (such that the mean of the distribution equals alpha divided by alpha plus beta). For gamma-distributed quantities, the shape and scale parameters are shown. For normally-distributed quantities, the mean and standard deviation are shown. For uniformly-distributed parameters, the minimum and maximum are shown. Distributions were selected and parameterized to best fit the means and uncertainty reported in Table 1 in the main text. *Truncated above at 100%; ** Truncated below at 1%; ***Truncated below at 0·5%; † Relevant for household contacts only; † † The estimated percent of HIV deaths in Africa that are related to TB from IHME's global burden of disease 58 CXR = chest Xray, LTFU = lost-to-follow-up.

Annual TB Reactivation
The probability of progression from latent infection to active disease was assumed to depend on time since entering the model, age (for household contacts only), and ART status (for PLWHA only). Estimates are shown in Appendix Table 4.

Appendix
For household contacts, progression is based on current age, not age upon which a contact entered the model. Year indicates the year since entering the model.

*All modeled cohorts start out on ART
We estimated annual progression probabilities of 10% [4-21%] for LTBI household contacts < 5 years and 5% [2-11%] for LTBI household contacts ≥ 5 years in the first two years since entering the model (i.e., the first two years after someone in their households was diagnosed with TB). These estimates were based on age-stratified pooled estimates of cumulative TB incidence among household contacts with positive TST/IGRA over 2 years from a recent meta-analysis of contact cohort studies by Martinez et al. (see their Figure 3A). 15 We estimated the relative decline in progression risk after two years based on estimates from a modeling study of TB progression over time by Menzies et al. 26 (see their Figure 1A) and a systematic review of cohort studies that measured TB reactivation > 2 years post-infection by Dale et al. 27 (see their Figure 2). Based on these two studies, the assumption that most household contacts with LTBI have been recently infected, and the assumption that after the first two years contacts originally < 5 years old would no longer be at higher risk of disease progression than older contacts, we estimated that annual progression risk among all household contacts would drop to 0·2% [0·1-0·3%] after 2 years, 0·1% [0·05-0·2%] after 5 years, and 0·08% [0·06-0·09%] after 10 years.
We estimated that PLWHA newly enrolled on ART would have a 7·4% [3·2-12·6%] chance of progressing to active disease in the first year since initiating ART and a 3·3% [1·6-5·6%] chance of progressing in the second year, based on progression to active disease among those with positive TST that were randomized not to receive IPT in a trial of the impact of ART and IPT on TB incidence by Golub et al. 16 In particular, we started with the 2-year probability of infection among non-IPT initiators with a positive TST (11·5%). We subtracted from this the probability of infection among IPT initiators with a positive TST (1·6%; to proxy incidence attributable to reinfections, rather than progression) and adjusted for the proportion of non-IPT initiators that had not yet initiated ART (26%) by applying the study-estimated relative hazard of developing TB without ART (vs. with ART; 0·41 [0·31-0·54]). This yielded a two-year probability of infection of 7·2% among ART initiators. For PLWHA who are newly enrolled on ART, who are assumed to be at a higher risk of progression than established PLWHA, we adjusted this two-year probability of infection upward by accounting for the percentage of patients in the trial that had received ART earlier on (76%) and the relative hazard of developing TB disease with higher vs. medium CD4 counts (0·54 [0·31-0·90]). This yielded an annual probability of progression of 5·6% among newly enrolled PLWHA. We then applied data from a Swiss cohort study authored by Elz et al. 32 to account for the higher incidence rate in year 1 (1·32 [1·19-1·45] relative risk vs. years 1 and 2 combined) compared to year 2 (0·59 [0·54-0·64] relative risk vs. years 1 and 2 combined). We propagated uncertainty in all estimates/trial-reported data to estimate that newly enrolled PLWHA with LTBI face an annual progression risk of 7·4% [3·2-12·6%] in the first year of observation and 3·3% [1·6-5·6%] in the second year of observation. We assumed that in years 1 and 2, established PLWHA face approximately half the risks of newly enrolled PLWHA (0·54 [0·31-0·90]), again based on the relative hazard by CD4 count estimates reported in Golub et al.
Based on outcomes among control groups from Golub et al. and the TEMPRANO trial of TPT among PLWHA 16,17 , we estimated that PLWHA not on ART continually (i.e., regardless of year of observation) face progression risks that are 2·5 [2-3] times that of PLWHA on ART.
After two years of observation, we assume that progression risks decline over time based on data from the Elz et al. Swiss cohort study (progression in years 2-10 was 0·12 [0·04-0·2] times that of progression in years 0-2). 32 After ten years of observation, we assumed that progression among PLWHA on ART relative to years 2-10 would be similar to the relative progression risk estimated for household contacts, above, based on Menzies et al. and Dale et al. 26,27 We intentionally used data from lower-burden settings to parameterize progression after the second year of observation to reduce the influence that reinfections would have on our estimates.

Willingness-to-Pay Threshold Details
Ochalek et al. provide estimates of country-specific cost per DALY thresholds in 2015 US dollars and as a percentage of 2015 GDP per capita, based on four different calculation methods. 60 We used the median percentage of GDP per capita across the four methods and applied the percentages to 2020 GDP per capita to calculate the updated cost-effectiveness thresholds in 2020 US dollars used in the main analysis. Given the evolving evidence on country-specific health opportunity costs, we also compared incremental cost-effectiveness ratios (ICERs) to the full range of thresholds (based on the percentage of GDP per capita ranges for each country from Ochalek et al.), gross national income per capita (less conservative), and $1000 (less conservative for lower-income countries, more conservative for higher-income countries). Describe what outcomes were used as the measure(s) of benefit(s) and harm(s).

Methods (paragraph 7)
Measurement of outcomes 12 Describe how outcomes used to capture benefit(s) and harm(s) were measured.
Methods (paragraphs 1-7, Table 1) Valuation of outcomes 13 Describe the population and methods used to measure and value outcomes.

14
Describe how costs were valued. Methods (paragraphs 1-4, Table 1) Currency, price date, and conversion 15 Report the dates of the estimated resource quantities and unit costs, plus the currency and year of conversion.

Methods (paragraph 7)
Rationale and description of model 16 If modelling is used, describe in detail and why used. Report if the model is publicly available and where it can be accessed.
Methods (paragraphs 1-4), Figure 1 Analytics and assumptions 17 Describe any methods for analysing or statistically transforming data, any extrapolation methods, and approaches for validating any model used.
Methods (paragraphs 3-4), Table 1, Appendix pages 3-13 Characterizing heterogeneity 18 Describe any methods used for estimating how the results of the study vary for subgroups.

Not Applicable
Characterizing distributional effects 19 Describe how impacts are distributed across different individuals or adjustments made to reflect priority populations.

Not Applicable
Characterizing uncertainty 20 Describe methods to characterise any sources of uncertainty in the analysis.

Methods (paragraph 7)
Approach to engagement with patients and others affected by the study 21 Describe any approaches to engage patients or service recipients, the general public, communities, or stakeholders (such as clinicians or payers) in the design of the study.

Study parameters
22 Report all analytic inputs (such as values, ranges, references) including uncertainty or distributional assumptions. Results (paragraphs 1-7), Figure 2, Figure 3 Effect of uncertainty 24 Describe how uncertainty about analytic judgments, inputs, or projections affect findings. Report the effect of choice of discount rate and time horizon, if applicable.
Results (paragraphs 8-9), Appendix pages 41-76 Effect of engagement with patients and others affected by the study 25 Report on any difference patient/service recipient, general public, community, or stakeholder involvement made to the approach or findings of the study

Not Applicable
Discussion Study findings, limitations, generalizability, and current knowledge 26 Report key findings, limitations, ethical or equity considerations not captured, and how these could affect patients, policy, or practice.

Appendix Figure 3: Average annual cost of a 3HP program, as a share of countries' annual TB budgets
Numbers shown in the figure were calculated as the cumulative costs under the 3HP scenario minus the cumulative costs under the no TPT scenario divided by thirteen years (to calculate average annual costs), divided by countries' total expected TB funding in 2021 (as reported to the WHO). For people with HIV (PWH), ART costs are not included as these costs would likely not be borne by the National TB Program. Numbers indicate means with 2·5 th and 97·5 th uncertainty intervals in brackets (except the last row, which shows medians and interquartile ranges across country means). Household contacts ages are displayed in years.     . The incremental cost-effectiveness ratios are plotted on cost-effectiveness frontiers depicting discounted costs in 2020 USD on the x-axis and discounted disability adjusted life years averted relative to the No TPT scenario on the y-axis. Strategies appearing on the frontier are shaded, while dominated and extended dominated strategies are unshaded (white fill). Shapes indicate whether TPT for PWH is included in the strategy, and colors indicate different contact age combinations. GNI p.c. = Gross National Income per capita.

Appendix Figure 5: Cost-effectiveness of contact investigation with 3HP versus contact investigation without 3HP for household contacts in 29 countries
Each marker represents a country-specific estimate of discounted incremental disability-adjusted life years (DALYs) averted per household contact from implementing 3HP with contact investigation (x-axis) and corresponding discounted incremental costs (y-axis), compared to a scenario of contact investigation but no TPT. Each panel shows a different target population (A. household contacts < 5 years old, B. contacts 5-14 years old, C. contacts ≥ 15 years old). Countries are labeled by their 3-digit ISO codes, and averages across the 29 countries are designated via a dark red star. Filled areas of the graph indicate incremental cost-effectiveness ratios. Countries for which 3HP was estimated to be cost-saving compared to contact investigation alone are not labeled on the graphs, but are listed in the text boxes at the lower left of each panel.

Summary of sensitivity of results to individual model parameters
Sensitivity of the cost-effectiveness results to individual model parameters was assessed using linear regression metamodeling. Results were sensitive to three main model parameters. 3HP for household contacts was more costeffective in settings with lower background notification rates and lower 3HP visit costs (Appendix Figures 6-9). For household contacts < 15, 3HP was generally more cost-effective in settings with lower TB treatment costs, because contact investigation tended to increase the numbers of people treated for TB. For household contacts ≥ 15 and PLWHA, 3HP was more cost-effective with higher TB treatment costs, because background treatment coverage ratios tend to be higher for older ages and thus the decrease in incidence from 3HP outweighed the increase in the notification rate from contact investigation (Appendix Figure 10). Cost-effectiveness for contacts < 15 was less sensitive to the price of 3HP than for contacts ≥ 15 and PLWHA, as 3HP made up a larger share of incremental costs for the latter populations (Appendix Figure 11).
Each panel depicts the preferred strategy at a given cost-effectiveness threshold ("CEA threshold") when all parameters are held at their mean values and a single parameter is adjusted over its full range. Thick black lines indicate the mean values for each parameter. The x-axis indicates the parameter value when it is scaled from 0% to 100%, with 0% representing the minimum, 100% representing the maximum, 50% representing the median, and so on. The x-axis locations where the graph changes color indicate the threshold of that parameter value at which the optimal strategy changes. Parameter minima and maxima are displayed on the plot margins. Parameter means and ranges of country-specific parameters (such as contact investigation costs and background notification rates) were obtained by pooling samples for each parameter separately over all 29 countries.
Each panel depicts the preferred strategy at a given cost-effectiveness threshold ("CEA threshold") when all parameters are held at their mean values and a single parameter is adjusted over its full range. Thick black lines indicate the mean values for each parameter. The x-axis indicates the parameter value when it is scaled from 0% to 100%, with 0% representing the minimum, 100% representing the maximum, 50% representing the median, and so on. The x-axis locations where the graph changes color indicate the threshold of that parameter value at which the optimal strategy changes. Parameter minima and maxima are displayed on the plot margins. Parameter means and ranges of country-specific parameters (such as contact investigation costs and background notification rates) were obtained by pooling samples for each parameter separately over all 29 countries.
Each panel depicts the preferred strategy at a given cost-effectiveness threshold ("CEA threshold") when all parameters are held at their mean values and a single parameter is adjusted over its full range. Thick black lines indicate the mean values for each parameter. The x-axis indicates the parameter value when it is scaled from 0% to 100%, with 0% representing the minimum, 100% representing the maximum, 50% representing the median, and so on. The x-axis locations where the graph changes color indicate the threshold of that parameter value at which the optimal strategy changes. Parameter minima and maxima are displayed on the plot margins. Parameter means and ranges of country-specific parameters (such as contact investigation costs and background notification rates) were obtained by pooling samples for each parameter separately over all 29 countries. parameter is adjusted over its full range. Thick black lines indicate the mean values for each parameter. The x-axis indicates the parameter value when it is scaled from 0% to 100%, with 0% representing the minimum, 100% representing the maximum, 50% representing the median, and so on. The x-axis locations where the graph changes color indicate the threshold of that parameter value at which the optimal strategy changes. Parameter minima and maxima are displayed on the plot margins. Parameter means and ranges of country-specific parameters (such as contact investigation costs and background notification rates) were obtained by pooling samples for each parameter separately over all 29 countries.
Figure shows how the incremental cost-effectiveness ratios (ICERs; y-axis) for each target population (colored lines, with household contact age groups displayed in years) vary when all parameters are held fixed at their mean values and the cost of a full course of TB treatment is varied from $10 to $5000 (xaxis). The points at which the colored lines cross the black horizonal lines indicate the 3HP price at which 3HP for a given population would be considered costeffectiveness based on the main cost-effectiveness thresholds in each country ("CEA threshold"; dashed line), cost-effective based on a threshold equal to gross national income per capita (GNI p.c.; dotted line), and cost-saving (solid line with a y-intercept of $0), respectively. "PWH" = people with HIV.
Figure shows how the incremental cost-effectiveness ratios (ICERs; y-axis) for each target population (colored lines, with household contact age groups displayed in years) vary when all parameters are held fixed at their mean values and the price of a full course of 3HP is varied from $0·1 to $50 (x-axis). The points at which the colored lines cross the black horizonal lines indicate the 3HP price at which 3HP for a given population would be considered costeffectiveness based on the main cost-effectiveness thresholds in each country ("CEA threshold"; dashed line), cost-effective based on a threshold equal to gross national income per capita (GNI p.c.; dotted line), and cost-saving (solid line with a y-intercept of $0), respectively. "PWH" = people with HIV.

Summary of sensitivity analysis results
Sensitivity analysis results are shown in Appendix Tables 18-21. In a sensitivity analysis that lowered eventual 3HP coverage to 50% (from 80% in the main analysis; "Lower TPT coverage"), 3HP was somewhat less cost-effective for PLWHA because it affected the annual proportions of PLWHA initiated on 3HP that were newly enrolled on ART vs. already established (newly enrolled PLWHA are at higher risk of progressing to active disease and are thus a more cost-effective population to cover). The cost-effectiveness of 3HP for household contacts was not affected by 3HP coverage.
In a sensitivity analysis that decreased ART turnover (resulting in overall higher adherence to ART; "Lower ART turnover"), 3HP was also somewhat less cost-effective for PLWHA -because PLWHA who discontinue ART are at greater risk of progressing to TB disease and thus receive more incremental benefit on average from taking 3HP.
1HP was found to be less cost-effective than 3HP in most settings, due to its increased drug costs. In some settings, the reduction in outpatient monitoring requirements and increased completion rates of 1HP outweighed the increased drug prices.
In an analysis that increased the number of required 3HP monitoring visits (with no improvement to 3HP outcomes), 3HP looked slightly less cost-effective but ICERs did not change substantially (generally < 10% increase for contacts).
The effect on cost-effectiveness of removing CXR from the screening algorithm for contacts aged 5 and above ("No CXR screen for HHCs") also varied by country. ICERs increased in countries where the costs of CXR relative to TB treatment were low and treatment coverage ratios were high because few savings were generated from the reduction in CXR costs, at the expense of fewer people with active TB linked to treatment.
3HP looked substantially more cost-effective when averted secondary transmission was factored into the analysis ("Secondary effects"). This was generally the sensitivity analysis that had the most dramatic impact on conclusions.
In most countries, the ICER of scaling up contact investigation without any TPT ("No TPT") was higher than the ICER of scaling up contact investigation with TPT, indicating that it is more cost-effective to include TPT when scaling up a contact investigation program.